Colors vs Real-World Objects

Basic Setup

── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

✓ ggplot2 3.3.2     ✓ purrr   0.3.4
✓ tibble  3.0.3     ✓ dplyr   1.0.2
✓ tidyr   1.1.2     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.5.0

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

Loading required package: lattice

Loading required package: ggformula

Loading required package: ggstance

Attaching package: 'ggstance'

The following objects are masked from 'package:ggplot2':

    geom_errorbarh, GeomErrorbarh

New to ggformula?  Try the tutorials: 
    learnr::run_tutorial("introduction", package = "ggformula")
    learnr::run_tutorial("refining", package = "ggformula")

Loading required package: mosaicData

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

Registered S3 method overwritten by 'mosaic':
  method                           from   
  fortify.SpatialPolygonsDataFrame ggplot2

The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

Have you tried the ggformula package for your plots?

Attaching package: 'mosaic'

The following object is masked from 'package:Matrix':

    mean

The following objects are masked from 'package:dplyr':

    count, do, tally

The following object is masked from 'package:purrr':

    cross

The following object is masked from 'package:ggplot2':

    stat

The following objects are masked from 'package:stats':

    binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
    quantile, sd, t.test, var

The following objects are masked from 'package:base':

    max, mean, min, prod, range, sample, sum

Attaching package: 'scales'

The following object is masked from 'package:mosaic':

    rescale

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

Loading required package: knitr

Import Colors

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)
Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  change = col_double(),
  responses = col_double(),
  time = col_double(),
  prev_condition = col_double(),
  prev_change = col_double(),
  color = col_double()
)

`summarise()` regrouping output by 'subject', 'age' (override with `.groups` argument)

Import Objects

Warning in rm(objectList): object 'objectList' not found

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]

Parsed with column specification:
cols(
  `#subject` = col_double(),
  block = col_double(),
  trial = col_double(),
  condition = col_double(),
  setsize = col_double(),
  change = col_double(),
  time = col_double(),
  responses = col_double(),
  time_1 = col_double(),
  Pic = col_character(),
  prev_condition = col_double(),
  prev_setsize = col_double(),
  prev_change = col_double(),
  prev_time = col_double()
)

Warning: Problem with `mutate()` input `pic_num`.
ℹ NAs introduced by coercion
ℹ Input `pic_num` is `as.numeric(Pic)`.

Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion

`summarise()` regrouping output by 'subject', 'age' (override with `.groups` argument)

`summarise()` regrouping output by 'subject', 'age', 'setsize' (override with `.groups` argument)

`summarise()` regrouping output by 'subject' (override with `.groups` argument)

`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)

join color & objects

colorListResult %>%ungroup() %>%  select(age,condition,mean_answer_correct,mean_response_time,kPashler,k,joinKey) -> ColorJoin
names(ColorJoin) <- c("age_c","condition_c","mean_answer_correct_c","mean_response_time_c","kPashler_c","k_c","joinKey") 
objectResult %>% inner_join(ColorJoin,by = "joinKey")  %>% 
  mutate(diffAnswerRate = mean_answer_correct-mean_answer_correct_c)%>% 
  mutate(diffK = k-k_c)-> testsJoined
objectResultCondition %>% inner_join(ColorJoin,by = "joinKey") -> testConditionJoined
colorListResult %>% ungroup() %>% select(subject,age,condition,mean_answer_correct,mean_response_time,kPashler,k) %>% mutate(test="color") %>% rename(setsize = condition) -> ColorJoinLong
objectResult %>%  ungroup() %>% select(subject,age,setsize,mean_answer_correct,mean_response_time,kPashler,k) %>% mutate(test="object") -> ObjectJoinLong
joinedTestList = rbind(ObjectJoinLong,ColorJoinLong)

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Erster Schritt Überblick der Gesamtanzahl der falschen und richtigen Antworten

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Gib eine Tabelle mit den zusammenfassenden Daten für Color & Objects aus

colorList %>% group_by(condition,age) %>% summarise(Gesamt = n(),Richtig = sum(answer_correct == 1),Falsch = sum(answer_correct == 0), ProzentRichtig = scales::percent(round(mean(answer_correct == 1),2)),ProzentFalsch = scales::percent(round(mean(answer_correct == 0),2))) %>% rename(SetSize = condition) %>% kable(caption = "Überblick Versuchsreihe Color",align = "llcccrr")

`summarise()` regrouping output by 'condition' (override with `.groups` argument)

Überblick Versuchsreihe Color

SetSize	age	Gesamt	Richtig	Falsch	ProzentRichtig	ProzentFalsch
2	old	1297	1226	71	95%	5%
2	young	1298	1268	30	98%	2%
4	old	1297	945	352	73%	27%
4	young	1303	1139	164	87%	13%
6	old	1291	863	428	67%	33%
6	young	1295	946	349	73%	27%
8	old	1315	782	533	59%	41%
8	young	1304	838	466	64%	36%

objectList %>% group_by(setsize,age) %>% summarise(Gesamt = n(),Richtig = sum(answer_correct == 1),Falsch = sum(answer_correct == 0), ProzentRichtig = scales::percent(round(mean(answer_correct == 1),2)),ProzentFalsch = scales::percent(round(mean(answer_correct == 0),2))) %>% kable(caption = "Überblick Versuchsreihe Real world objects",align = "llcccrr")

`summarise()` regrouping output by 'setsize' (override with `.groups` argument)

Überblick Versuchsreihe Real world objects

setsize	age	Gesamt	Richtig	Falsch	ProzentRichtig	ProzentFalsch
2	old	3840	3539	301	92%	8%
2	young	3840	3607	233	94%	6%
4	old	3840	3066	774	80%	20%
4	young	3840	3249	591	85%	15%
6	old	3840	2678	1162	70%	30%
6	young	3840	2911	929	76%	24%

#colorListResult %>% ungroup() %>% group_by(age,condition) %>% summarise(mean(hitRate),mean(falseAlarmRate),mean(k),mean(kPashler))%>% kable(caption = "Überblick Versuchsreihe Color objects K",align = "llcccrr")

Plotte einen Überblick der Gesamtmenge

colorList %>% mutate(age=factor(age, levels = c("young","old")),
             answer_correct=factor(answer_correct, levels = c(0,1),labels = c("False","Correct"))) %>%
  group_by(age,condition,answer_correct) %>% summarise(N=n()) %>% ungroup() %>%
  group_by(age,condition) %>% 
  mutate(Total=sum(N),Percent=N/Total,
         Lab=paste0(N,' (',paste0(round(100*Percent,0),'%'),')')) -> SumsColors

`summarise()` regrouping output by 'age', 'condition' (override with `.groups` argument)

#Plot
ggplot(SumsColors,aes(x=age,y=N,fill=answer_correct))+
  geom_bar(stat='identity',position = position_stack())+
  facet_wrap(.~condition,scales = 'free')+
  geom_text(aes(label=Lab),position = position_stack(vjust = .5),size=3)+
  geom_text(aes(y=Total,label=Total),vjust=-0.25,size=3)+
  labs(title="Colors",subtitle = "per setsize grouped by age", x="age", y="# answer correct", fill="Answer")+scale_fill_brewer(palette = "Pastel1")+
  ylim(0, 1400)

objectList %>% mutate(age=factor(age, levels = c("young","old")),
             answer_correct=factor(answer_correct, levels = c(0,1),labels = c("False","Correct"))) %>%
  group_by(age,setsize,answer_correct) %>% summarise(N=n()) %>% ungroup() %>%
  group_by(age,setsize) %>% 
  mutate(Total=sum(N),Percent=N/Total,
         Lab=paste0(N,' (',paste0(round(100*Percent,0),'%'),')')) -> SumsObjects

`summarise()` regrouping output by 'age', 'setsize' (override with `.groups` argument)

#Plot
ggplot(SumsObjects,aes(x=age,y=N,fill=answer_correct))+
  geom_bar(stat='identity',position = position_stack())+
  facet_wrap(.~setsize,scales = 'free')+
  geom_text(aes(label=Lab),position = position_stack(vjust = .5),size=3)+
  geom_text(aes(y=Total,label=Total),vjust=-0.25,size=3)+
  labs(title="Real world objects",subtitle = "per setsize grouped by age", x="age", y="# answer correct", fill="Answer")+scale_fill_brewer(palette = "Pastel1")

Plotte die Streuung von Colors und real objects

colorListResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")

Häufigkeitsverteilung

colorListResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins = 20)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

colorListResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+facet_wrap(.~condition,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")

objectResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

objectResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+facet_wrap(.~setsize,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")

colorListResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density (alpha=0.2)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

colorListResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density( alpha=0.2)+facet_wrap(.~condition,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")

objectResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density(alpha=0.2)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

objectResult %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density(alpha=0.2)+facet_wrap(.~setsize,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")

Vergleich Color vs. Objects gleiche Altersgruppen

joinedTestList %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=test))+ geom_density (alpha=0.2)+labs(title="Colors vs Object",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

joinedTestList %>%  ggplot(mapping=aes(x=mean_answer_correct, fill=test))+ geom_density (alpha=0.2)+facet_wrap(.~setsize,scales = 'free')+labs(title="Colors vs Object",subtitle = "Häufigkeiten korrekter Antworten per Set Größe", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)

Korrelation

colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)

`geom_smooth()` using formula 'y ~ x'

colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = "loess", span = 0.15, method.args = list(degree=1))

`geom_smooth()` using formula 'y ~ x'

colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)+facet_wrap(.~condition,scales = 'free')

`geom_smooth()` using formula 'y ~ x'

objectResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)

`geom_smooth()` using formula 'y ~ x'

objectResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)+facet_wrap(.~setsize,scales = 'free')

`geom_smooth()` using formula 'y ~ x'

objectResult %>% filter(age==“young”,setsize==6) %>% ungroup %>% select(mean_answer_correct) -> testIt shapiro.test(testIt$mean_answer_correct)

Normalverteilung:

colorListResult %>% filter(condition==2,age=="old") -> df_means_o2
fromTo <- round(range(df_means_o2$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o2$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 2")
rug(jitter(df_means_o2$mean_answer_correct))
curve(dnorm(x,mean(df_means_o2$mean_answer_correct),sd(df_means_o2$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==4,age=="old") -> df_means_o4
fromTo <- round(range(df_means_o4$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o4$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 4")
rug(jitter(df_means_o4$mean_answer_correct))
curve(dnorm(x,mean(df_means_o4$mean_answer_correct),sd(df_means_o4$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==6,age=="old") -> df_means_o6
fromTo <- round(range(df_means_o6$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o6$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 6")
rug(jitter(df_means_o6$mean_answer_correct))
curve(dnorm(x,mean(df_means_o6$mean_answer_correct),sd(df_means_o6$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==8,age=="old") -> df_means_o8
fromTo <- round(range(df_means_o8$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o8$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 8")
rug(jitter(df_means_o8$mean_answer_correct))
curve(dnorm(x,mean(df_means_o8$mean_answer_correct),sd(df_means_o8$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==2,age=="young") -> df_means_y2
fromTo <- round(range(df_means_y2$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y2$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 2")
rug(jitter(df_means_y2$mean_answer_correct))
curve(dnorm(x,mean(df_means_y2$mean_answer_correct),sd(df_means_y2$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==4,age=="young") -> df_means_y4
fromTo <- round(range(df_means_y4$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y4$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 4")
rug(jitter(df_means_y4$mean_answer_correct))
curve(dnorm(x,mean(df_means_y4$mean_answer_correct),sd(df_means_y4$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==6,age=="young") -> df_means_y6
fromTo <- round(range(df_means_y6$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y6$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 6")
rug(jitter(df_means_y6$mean_answer_correct))
curve(dnorm(x,mean(df_means_y6$mean_answer_correct),sd(df_means_y6$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

colorListResult %>% filter(condition==8,age=="young") -> df_means_y8
fromTo <- round(range(df_means_o8$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y8$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 8")
rug(jitter(df_means_y8$mean_answer_correct))
curve(dnorm(x,mean(df_means_y8$mean_answer_correct),sd(df_means_y8$mean_answer_correct)),lwd=2,col="blue",add=TRUE)

K Wert

# Color List
colorListResult %>% ungroup() %>% group_by(age,condition) %>%summarise(round(mean(hitRate),2),round(sd(hitRate),2),round(mean(falseAlarmRate),2),round(sd(falseAlarmRate),2),round(mean(k),2),round(sd(k),2),round(mean(kPashler),2),round(sd(kPashler),2)) ->df_KStat_data

`summarise()` regrouping output by 'age' (override with `.groups` argument)

names(df_KStat_data) <- c("age","setsize","mean_hit_rate","sd_hit_rate","mean_false_alarm","sd_false_alarm","mean_k","sd_k","mean_kPashler","sd_kPashler")
colorListResult %>% ungroup() %>% group_by(age,condition) %>%summarise(paste(round(mean(hitRate),2),"/",round(sd(hitRate),2)),paste(round(mean(falseAlarmRate),2),"/",round(sd(falseAlarmRate),2)),paste(round(mean(k),2),"/",round(sd(k),2)),paste(round(mean(kPashler),2),"/",round(sd(kPashler),2))) ->df_KStat

`summarise()` regrouping output by 'age' (override with `.groups` argument)

names(df_KStat) <- c("age","Set Size","HitRate Mean/SD","False Alarm Mean/SD","k Mean (SD)","kPashler MEAN/SD")
df_KStat %>% kable(caption = "Überblick Versuchsreihe Color objects K",align = "lcccccr")

Überblick Versuchsreihe Color objects K

age	Set Size	HitRate Mean/SD	False Alarm Mean/SD	k Mean (SD)	kPashler MEAN/SD
old	2	0.95 / 0.05	0.06 / 0.05	1.78 / 0.15	1.88 / 0.11
old	4	0.82 / 0.11	0.37 / 0.17	1.83 / 0.69	2.85 / 0.78
old	6	0.8 / 0.16	0.47 / 0.2	2.02 / 0.71	4.09 / 1.28
old	8	0.77 / 0.16	0.58 / 0.18	1.51 / 0.82	3.77 / 2.19
young	2	0.99 / 0.02	0.03 / 0.03	1.91 / 0.07	1.97 / 0.04
young	4	0.95 / 0.03	0.21 / 0.11	2.99 / 0.43	3.77 / 0.17
young	6	0.89 / 0.07	0.43 / 0.16	2.76 / 1	4.72 / 0.9
young	8	0.87 / 0.07	0.58 / 0.12	2.27 / 0.72	5.52 / 0.98

# Object List
objectResult %>% ungroup() %>% group_by(age,setsize) %>%summarise(round(mean(hitRate),2),round(sd(hitRate),2),round(mean(falseAlarmRate),2),round(sd(falseAlarmRate),2),round(mean(k),2),round(sd(k),2),round(mean(kPashler),2),round(sd(kPashler),2)) ->df_O_KStat_data

`summarise()` regrouping output by 'age' (override with `.groups` argument)

names(df_O_KStat_data) <- c("age","setsize","mean_hit_rate","sd_hit_rate","mean_false_alarm","sd_false_alarm","mean_k","sd_k","mean_kPashler","sd_kPashler")
objectResult %>% ungroup() %>% group_by(age,setsize) %>%summarise(paste(round(mean(hitRate),2),"/",round(sd(hitRate),2)),paste(round(mean(falseAlarmRate),2),"/",round(sd(falseAlarmRate),2)),paste(round(mean(k),2),"/",round(sd(k),2)),paste(round(mean(kPashler),2),"/",round(sd(kPashler),2))) ->df_O_KStat

`summarise()` regrouping output by 'age' (override with `.groups` argument)

names(df_O_KStat) <- c("age","Set Size","HitRate Mean/SD","False Alarm Mean/SD","k Mean (SD)","kPashler MEAN/SD")
df_O_KStat %>% kable(caption = "Überblick Versuchsreihe Real World Objects K",align = "lcccccr")

Überblick Versuchsreihe Real World Objects K

age	Set Size	HitRate Mean/SD	False Alarm Mean/SD	k Mean (SD)	kPashler MEAN/SD
old	2	0.87 / 0.07	0.03 / 0.03	1.69 / 0.13	1.74 / 0.13
old	4	0.77 / 0.07	0.17 / 0.07	2.39 / 0.27	2.89 / 0.31
old	6	0.75 / 0.12	0.36 / 0.13	2.37 / 0.46	3.76 / 0.85
young	2	0.92 / 0.04	0.04 / 0.03	1.76 / 0.1	1.82 / 0.09
young	4	0.83 / 0.05	0.14 / 0.07	2.77 / 0.31	3.22 / 0.22
young	6	0.82 / 0.07	0.3 / 0.1	3.1 / 0.5	4.47 / 0.52

#colorListResult %>% ungroup %>% group_by(age,condition) %>% summarise(round(max(k),2)) %>% kable(caption = "Überblick Versuchsreihe Colors k Wert",align = "lcr")
#colorListResult %>% ungroup %>% group_by(age,condition) %>% summarise(round(max(kPashler),2)) %>% kable(caption = "Überblick Versuchsreihe Colors kPashler Wert",align = "lcr")
#objectResult %>% ungroup %>% group_by(age,setsize) %>% summarise(round(max(k),2)) %>% kable(caption = "Überblick Versuchsreihe Real world objects k Wert",align = "lcr")
#objectResult %>% ungroup %>% group_by(age,setsize) %>% summarise(round(max(kPashler),2)) %>% kable(caption = "Überblick Versuchsreihe Real world objects kPashler Wert",align = "lcr")

ggplot(df_KStat_data, aes(x=factor(setsize), y=mean_k, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_k-sd_k, ymax=mean_k+sd_k),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k)") + scale_fill_hue(name="Age Group") + ggtitle("Color Test K value results per set size")

ggplot(df_O_KStat_data, aes(x=factor(setsize), y=mean_k, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_k-sd_k, ymax=mean_k+sd_k),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k)") + scale_fill_hue(name="Age Group") + ggtitle("Real World Objects Test K value results per set size")

ggplot(df_KStat_data, aes(x=factor(setsize), y=mean_kPashler, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_kPashler-sd_kPashler, ymax=mean_kPashler+sd_kPashler),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k-Pashler)") + scale_fill_hue(name="Age Group") + ggtitle("Color Test k-Pashler value results per set size")

ggplot(df_O_KStat_data, aes(x=factor(setsize), y=mean_kPashler, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_kPashler-sd_kPashler, ymax=mean_kPashler+sd_kPashler),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k-Pashler)") + scale_fill_hue(name="Age Group") + ggtitle("Real World Objects Test k-Pashler value results per set size")

Grafiken Für K-Werte

colorListResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

colorListResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

objectResult %>%  ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")

####### K Barcharts ##########
mkChart <- ggplot(colorListResult, aes(x = condition,y = k, col = factor(age), fill = factor(age)))

Unterschied zwischen Colors & Objekte H1: Änderungen von Real World Objekten werden von jungen Personen besser erkannt als Änderungen von Farben. H1: Änderungen von Real World Objekten werden von alten Personen besser erkannt als Änderungen von Farben. Notwendige Auswertungen: T-Test abhängige Stichprobe je alt und jung

Voraussetzungen:

Abhängige Variable ist intervallskaliert - ok Es liegen zwei verbundene Stichproben vor aber die Meßwertpaare sind unabhängig - ok Die Unterschiede zwischen den verbundenen Testwerten sind in der Grundgesamtheit normalverteilt.

colorList %>% filter(condition <= 6) %>% summarise(mean(answer_correct)) -> df_mean_color_all